{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working With XMILE System Dynamics Models\n",
    "\n",
    "In this brief document we take a look at the quickest route to importing a System Dynamics model stored in the XMILE format, such as those created with ®Stella (`*.stmx`) or ®iThink (`*.itmx`).\n",
    "\n",
    "The main thing you need to understand here is that in its standard configuration, the BPTK-Py framework transpiles such models into Python code automatically in the background.\n",
    "\n",
    "So the code you actually work with in the Jupyter notebook is the transpiled Python model.\n",
    "\n",
    "BPTK-Py watches the original XMILE file to see whether you have changed the model in the modeling environment – if yes, the model is automatically re-transpiled.\n",
    "\n",
    "For this mechanism to work, you need to tell BPTK-Py about your model and for this you need to create a scenario file.\n",
    "\n",
    "In the standard configuration, BPTK-Py expects to find both a `scenarios` folder (which contains the scenario files) and a `simulation_models` folder which initially contains the XMILE models and – once BPTK-Py has been started – also the transpiled Python models.\n",
    "\n",
    "You can see this directory structure in our tutorial. This tructure can be changed in the BPTK-Py configuration settings, but let's work with the standard settings for now.\n",
    "\n",
    "To illustrate the process, let's assume you want to work with the `sd_simple_project.itmx` model for the first time. You have set up your Python environment and installed all the necessary packages (read our [installation instructions](https://bptk.transentis.com/en/latest/docs/usage/installation.html) if you haven't done this yet)\n",
    "\n",
    "Once you have your working enviornment, setting up your first notebook only takes a few minutes if you follow these six steps:\n",
    "\n",
    "1. Create the directory structure\n",
    "2. Place your model in the simulation_models directory\n",
    "3. Create an initial scenario file\n",
    "4. Create an initial Jupyter notebook\n",
    "5. Initialize BPTK-Py\n",
    "6. Start experimenting with your model\n",
    "\n",
    "## Step 1: Create The Directory Structure\n",
    "\n",
    "Create a `scenarios` and `simulation_models` directory in your working directory (right next to the `venv` or `virtualenv` directory if you are working with a virtual Python environment).\n",
    "\n",
    "## Step 2: Place Your Model In The Simulation_Models Directory\n",
    "\n",
    "Place your model in the `simulation_models` directory. In this example this is the `simulation_models/sd_simple_project.itmx` model.\n",
    "\n",
    "## Step 3: Create An Initial Scenario File\n",
    "\n",
    "Next you create an inital scenario file and place it in the `scenarios` directory.\n",
    "\n",
    "The most minimal scenario file defines the source model, the name of the transpiled model and an empty scenario. You can create a scenario file using JSON or YAML, this example uses JSON.\n",
    "\n",
    "It doesn't matter what name you give your scenario file – in our tutorial, we have named the file `minimal.json`.\n",
    "\n",
    "\n",
    "```javascript\n",
    "{\n",
    "    \"smWorkingWithXMILE\":\n",
    "    {\n",
    "        \"source\":\"simulation_models/sd_simple_project.itmx\",\n",
    "        \"model\":\"simulation_models/sd_simple_project\",\n",
    "        \"scenarios\":\n",
    "        {\n",
    "          \"base\": { }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "```\n",
    "\n",
    "By convention, we mostly name the python model file (`model` to match the name of the XMILE source file (`source`), but this is just a convention.\n",
    "\n",
    "The `smWorkingWithXMILE` is the name of the so-called scenario manager which is created by BPTK-Py upon reading the scenario file. Scenario managers are used to store your scenarios. You can give your scenario manager any name you would like, we use the prefix `sm`by convention.\n",
    "\n",
    "For this example, we have created a sd_simple_project.json containing the above code and placed it in the `scenarios` directory.\n",
    "\n",
    "## Step 4: Create A Jupyter Notebook\n",
    "\n",
    "In the next step you need to create a Jupyter notebook which will contain your scenario experiments. Place this notebook directly in your working directory. \n",
    "\n",
    "> Important: Of course you can also work directly in the Python console or IPython, but we highly recommend Jupyter Lab.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Initialize BPTK-Py\n",
    "\n",
    "Now we are ready to initialize BPTK-Py. BPTK-Py automatically reads all the scenario files from the `scenarios` directory and transpiles all the necessary models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Important: bptk must be started from the directory containing the `scenarios` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import bptk\n",
    "bptk = bptk()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now have a `sd_simple_project.py` file in the `simulation_models` directory. This contains your System Dynamics model in Python code.\n",
    "\n",
    "> Important: If you are working directly in the `bptk_py_tutorial` folder, there will already be a sd_simple_project.py file. As this is an automatically generated file, you can safely delete it. It will be re-generated as soon as you restart BPTK-Py or change the XMILE model.\n",
    "\n",
    "It is worth taking a look at the file, because you can see the names of your equations here. BPTK-Py automatically converts your model variable names (such as `Open Taks`) into names that are compliant with Python (such as `openTasks`). Here is what the Python code for `Open Tasks` looks like:\n",
    "\n",
    "```\n",
    "'openTasks': lambda t : ( (self.memoize('initialOpenTasks', t)) if ( t  <=  self.starttime ) else (self.memoize('openTasks',t-self.dt) +  self.dt  * ( -1 * ( self.memoize('completionRate',t-self.dt) ) )) )\n",
    "```\n",
    "\n",
    "### Show available equations\n",
    "\n",
    "We know that the automatic renaming of equations might be confusing for\n",
    "some users, who enjoy the freedom of XMILE when it comes to naming.\n",
    "\n",
    "That's why we decided to give you a new function call that lists all equations for System Dynamics Models.\n",
    "\n",
    "Simply run ``bptk.list_equations()`` and optionally the scenario manager(s) and scenario(s) you'd like to see the available equations for.\n",
    "\n",
    "For example, let us get an overview over all equations and model elements for the scenario manager ``smWorkingWithXMILE``:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available Equations:\n",
      "\n",
      "Scenario Manager: smWorkingWithXMILE\n",
      "Scenario: base\n",
      "------------------\n",
      "\tstock: \t\t\tclosedTasks\n",
      "\tstock: \t\t\topenTasks\n",
      "\tstock: \t\t\tstaff\n",
      "\tflow: \t\t\tcompletionRate\n",
      "\tconverter: \t\tcurrentDate\n",
      "\tconverter: \t\tdeadline\n",
      "\tconverter: \t\teffortPerTask\n",
      "\tconverter: \t\tinitialOpenTasks\n",
      "\tconverter: \t\tinitialStaff\n",
      "\tconverter: \t\tremainingTime\n",
      "\tconverter: \t\tschedulePressure\n",
      " \n"
     ]
    }
   ],
   "source": [
    "bptk.list_equations(scenario_managers=[\"smWorkingWithXMILE\"],scenarios= [])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, we left the argument ``scenarios`` empty. So we are trying to load all possible scenarios for the given scenario manager. You can also leave ``scenario_managers`` empty to get a full overview over the available SD scenarios and elements!\n",
    "\n",
    "## Step 6: Start Experimenting With Your Model\n",
    "\n",
    "We are actually finished with the import now and we can start playing with the model. Here is some code to plot the `Open Tasks` and `Closed Tasks`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenario_managers=[\"smWorkingWithXMILE\"],\n",
    "    scenarios=[\"base\"], \n",
    "    equations=['openTasks','closedTasks']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finished\n",
    "\n",
    "That was it: creating the initial setup for working with XMILE files just takes a few minutes. Create a minimal scenario file, run two Python statements, then you are ready to start experimenting!"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5721abfe33942d351f7b09905420df100ba8c34b49fc290848df1028bfa2da07"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
